A data analytics strategy guide for accelerated data governance with cataloging

Research suggests 2.5 Quintilian bytes of data gets produced every day. The rapid proliferation of data has made it extremely difficult for enterprises to efficiently manage and find the right information when needed. Due to the generation of such large data volumes and the lack of a cohesive data analytics strategy, enterprises end up wasting a lot of time and effort on finding and accessing data from ‘data swamps’. Lack of a common business vocabulary, complex methods to assess provenance, quality, and trustworthiness, difficulty in understanding ‘dark data’ and numerous regulations to abide by have further turned data accessibility into a severely tangled knot.

With modern enterprises increasingly relying on big data and analytics for decision making, implementing an effective data governance process has evolved as a top priority. A well-designed data analytics strategy for an effective data governance program can help companies successfully capitalize on data and use it to drive tangible business outcomes. The need of the hour is to thus deploy a robust data governance framework that aligns with the future business objective and business models of the company.

The guidebook will explain the importance of data governance for enterprises, ways to achieve simpler and consolidated access to data assets, and how this can be facilitated by data catalogs.

On an enterprise level, implementing data governance effectively translates to defining the authority of data control and utilization. In essence, it is about managing the roles, responsibilities and the overarching processes that ensure ownership and accountability of the organizational data assets.

A well-managed data governance framework can successfully underpin an organization’s journey towards operating on digital platforms. However, creating such a framework requires a process to deal with common imperatives surrounding data, such as:

Building a robust data governance framework

As such, data governance frameworks support an organization’s strategy to manage data. From data collection, management, security, and storage, data governance frameworks cover end-to-end enterprise data lifecycle. An effective data governance framework must account for:

Fig 1: Sigmoid’s Approach to Data Governance

Data operations management

An effective data operations management framework with data modelling and designing capabilities for data analysis, pipeline building, testing, and maintenance.

Data risk & security management

Data privacy, confidentiality, and access control for data security and risk management along with deployment and management of structured data storage.

Data provenance & lineage

Data provenance and lineage for data source identification, re-enactment of data flow for updates and tracking of errors throughout the data lifecycle. This spans across data integration and extraction, transformation, replication and virtualization.

Data catalog management

Data discoverability, and search for complete data visibility and automatic data classification based on context using comprehensive metadata management.

Data quality management

Improving the fitness of data through, holistic definition, monitoring, and maintenance to achieve accurate, complete and consistent data for downstream analytics.

Regulatory compliance

Standardized definition and usage of shared data values to ensure regulatory compliance, data quality and analytical data processing management to foster Business Intelligence (BI).

A data catalog is a core component of data governance and an integral part of an organization's broader data analytics strategy. It makes use of metadata to provide organizations with a single, overarching view along with deeper visibility into their data assets. A data catalog is essentially a cluster of metadata combined with high-end data management and search tools. These search tools allow data users and analysts to locate specific data for intended use cases. This can help organizations efficiently manage their valuable data and get easy access to trusted data as and when required. Technologies like AI and machine learning have greatly diversified the use-cases of metadata. Technical, business, and operational metadata have undergone a mini-revolution and have found usage beyond audit, lineage, and reporting.

Today, metadata can augment data management in almost every possible way, be it for self-service data preparation, alerting anomalies, or auto-scaling resources. And the data catalogs leverage this metadata to enable data scientists to gain an edge.

Data cataloging principles are codes or guiding rules that must be followed by the data catalog users. The following principles form the core of such directives set for the users and catalogers:

Fig 2: Data Cataloging Principles

Intelligent data catalog adoption can ensure faster data discovery, lesser time to generate insights, and reduced time-to-market only if companies know the way to generate the greatest value from it. Here is a step-by-step process developed on the basis of road-tested best practices to simplify the adoption of data catalog:

Fig 3: Best Practices to Adopt Data Catalog

In the past few years, cloud, big data analytics, AI, and machine learning have transformed how data scientists manage, leverage, and access data. Data scientists now rely on data quality to a greater extent. Good data cataloging can serve this purpose in so many ways because fundamentally it provides wider visibility and deeper access to quality data. Here are just a few use cases of the same:

Most data scientists struggle in finding the right data and then face trouble in understanding whether it’s useful. Data cataloging can help them understand the business context around data elements. This will ensure that data scientists have information about the data source, its relationship with other data assets, and other crucial information like whether it’s a managed resource or if it’s from the right data source. These elements can convey an understanding about something as simple as statistical information, to something as complex as personal information.

Data cataloging can help demonstrate the provenance of data and provide detailed data lineage. It can ensure that data artifacts come from the right source and get transformed before reaching the final target. This can also help data scientists understand how changes introduced in a particular data pipeline can impact other related ones.

Most organizations adopt a specific vocabulary and a consistent understanding of business concepts. Having a data catalog can help store and manage this critical information. It links business terms to establish a taxonomy. This can help data scientists understand which business concepts correlate to which technical artifacts and then see everything related to their data.